ECE 382V Human Signals: Sensing and Analytics (Spring 2023)
Unique: 17710
Time: MW 3:00 to 4:30pm
Location: CPE 2.212
Instructor: Edison Thomaz (ethomaz at utexas dot edu, or contact through Canvas)
Office Hours: Wednesdays 1-2pm or by appointment
Office Hours Location: EER 7.818
TA: Rebecca Adaimi (rebecca dot adaimi at utexas dot edu, or contact through Canvas)
Office Hours: Mondays 4:30-5:30pm or by appointment
Office Hours Location: TBD
This course is aimed at graduate students and has 2 objectives. The first one is to teach concepts and practical skills for building systems that can sense and infer human signals (behavioral, physiological, emotional) and their respective context while leveraging mobile, ubiquitous and wearable computing technologies. The second aim is to examine and discuss advanced and emerging topics in the field in a seminar-style setting. Students will work on assignments throughout the semester, complete a project in a topic of their choosing, read and critique papers, present papers in class, and lead discussions. Key topics covered include machine learning fundamentals, activity recognition, sensing approaches (on-body, environmental), sensing modalities (e.g., inertial, acoustic, vision), sensor signal processing, and digital phenotyping.
Requirements
Academic: Graduate standing (or undergraduate with instructor approval). At a basic level, students will be expected to be comfortable using a high-level programming language. Experience with machine learning and related toolkits such as scikit-learn as well as mobile programming (iOS or Android) is useful but not required; we will review key concepts in the first part of the course. All the programming work in this class will be in Python and the Anaconda scientific package. If you are unsure if your background is a good match for this course, please come talk to the instructor.
Writing and Speaking: This course involves significant speaking and writing skills. Assignments must be completed in English. While primary assessment will focus on course material, correct spelling and grammar (along with coherence and logic) are expected and will be assessed in grading. Also, everyone is expected to participate in class and should be prepared for it. Class discussion each week is intended to reinforce understanding of the material. UT offers individual writing consultations to graduate students through the University Writing Center. Take advantage of these services, particularly before handing in project assignments (this of course requires starting early to get feedback from consultations and revise drafts accordingly).
Equipment: It is assumed that all students will have access to a computing system (i.e. laptop) to work on assignments. If you need assistance with computing resources, please contact the instructor.
Reading Materials
This class focuses on cutting-edge research in the field. Therefore, most of what we will read and discuss are recently-published papers. These will be provided to you by the instructor.
At a high-level, there are 5 key activities and deliverables that students will be responsible for in this course:
Read the assigned paper for each day and write a short critique of the paper (plus two discussion points)
Present and lead an in-class discussion for one or two "perspective" papers on topics of your choice
Complete four technical assignments that involve coding, analysis, and possibly data collection
Complete a project in the 2nd part of the course. Deliverables include a proposal, a progress update, final report, and a final presentation
Participate in class discussions
Specific details about these activities and deliverables are provided in the sections below.
Paper Reading: Critique + Discussion Points
All students in the class will be expected to read the required paper assigned for each lecture. Additionally, each student will be expected to submit a one or two paragraph critique of the paper and two discussion points. A few discussion points will be selected for class discussion. These points could be elements of the paper that you did not understand or specific questions that emerged while you were reading the paper (e.g., about the methodology, instrumentation, user study, data analysis, motivation). To write your critique and come up with the two discussion points, here are some examples of questions you could ask yourself while reading and examining papers:
Is the work well motivated?
What kind of real-world impact can it have?
Are the methods and approaches sound?
How does the research compare to prior work? Is it evolutionary?
Are the experiments well formulated and conducted?
What main problems or research questions are raised?
Are the claims well-supported?
What different assumptions are being made?
Does the work generalize to other situations, individuals?
What research questions are left open?
You should post your critique paragraphs using the following form. The deadline for these will always be 10am on the day of the lecture. You will be graded on your communication and the quality and depth of your critique and discussion points.
Perspective Papers: Presentation and Discussion
Twice during the semester, each student will be assigned a paper to present to the class. The presentation should be equivalent to a 10-minute conference-style talk, with 5 additional minutes for discussion, for a total of 15 minutes per paper. As the paper “expert”, the student presenter will be required to read the paper in detail and prepare (1) a set of slides to show in class, and (2) be ready to answer clarification questions. We will do our best to match students with their topic of choice; this assignment will be established in the first two weeks of class. Students should consider these presentations to be like exams. If a student is not available to present his or her assigned paper (without advance notification), the student will get zero credit for this portion of the course grade. Students should communicate with the instructor at least one week before the scheduled presentation if they will not be available to present on the assigned day.
Assignments
Students will work on four assignments during the semester. The assignments will be due approximately 10 days after they have been made available. The assignments will be on the topics of machine learning, inertial sensing, acoustic sensing and vision sensing.
Project
Students will be required to complete a class project, either individually or in pairs. Projects will let students choose a particular topic of their interest and study it in more depth. It is not expected to represent original research but students are encouraged to think creatively. It is ok to build on previous ideas and studies, but it is not appropriate to simply replicate previous work. Project ideas will be provided to you by the instructor. Three deliverables will be expected as part of the project: (1) a proposal, (2) a final report, and (3) a 10-minute conference-style presentation with slides. If forming a team, it is recommended that the team is in place 2 weeks before the proposal is due.
Proposal
The first project deliverable will be the project proposal, which should be no longer than 2 pages and include all the sections below. The proposal will be graded on the basis of completeness and clarity for each one of the sections, as well as novelty. Students should discuss their project and proposal with the instructor and/or the TA before submission.
Title
Team Member(s):
Description: What you are going to do and how
Motivation: Why this is an interesting problem
Description of previous research in the area (with references)
Resources you may need (in terms of hardware, software, etc)
Expected timeline
References
Update
The project update should be no longer than 2 pages and include all the sections below. The goal of this document is to communicate how your project is progressing, whether you are running into unanticipated challenges, and what you are planning to do to mitigate these challenges.
Title
Team Member(s)
Tasks Completed: What has been done
Tasks Remaining: What is left to do
Challenges: Outstanding issues and how they will be addressed
Timeline: Revised timeline if applicable
References
Final Report
The report should not be longer than 10 pages (one-column) plus references, following the ACM double-column format. Graphs and images are ok. You may submit appendices which include design documents or other diagrams such as circuit layouts. These will not count towards the page limit. Refer to the papers we have read in class for pointers on how to present your work in writing. Links to the Latex and Word templates for this format can be found here. At a minimum, your paper must include the sections below:
Title
Team Member(s)
Introduction: What are are doing, why is is important?
Prior work: What has already been done in this field?
Description of the work, implementation details, data analysis, etc.
Contributions: What are the original elements of the work?
Discussion: What have you learned
Future Work: What remains to be done for your project?
Conclusions: Take home points
References
Final Presentation
At the end of the semester our class will be dedicated to project presentations. Students will be expected to deliver a 15-minute conference-style presentation with slides and then answer questions afterwards for 5 minutes. The presentations will be expected to include a technical description of the project and motivation, methods useds, related work, and key take ways, e.g., what you learned. Clarity of communication and quality of the presentation slides will also be part of the grading criteria.
Class Participation
Students are expected to attend every lecture and participate in discussions. Participation is not optional; the instructor will actively engage with students throughout the semester. The class participation grade will be assigned to every student based on this engagement.
Grading
Here is a breakdown of how the final grade for each student will be computed:
Class Participation: 10%
Paper Critiques: 10%
Paper Presentation and Moderation: 10%
Assignment #1: 10%
Assignment #2: 10%
Assignment #3: 10%
Assignment #4: 10%
Project Proposal: 5%
Project Update: 5%
Project Final Report: 10%
Project Final Presentation: 10%
Grade Distribution: A (90-100), B (80-89), C (70-79), D (60-69), Fail (<60)
Contesting Grades
Students may request a regrade on a deliverable only within one week of a grade being assigned.
Late Deliverables
Late deliverables will be accepted for one week after their due date. This will apply to assignments, the project proposal and the project update. Within this week, deliverables will be graded but 30 points (out of 100 points) will be automatically deducted. After one week, assignments will receive a zero. In the interest of fairness, there will not be any exceptions to this policy.
Absence Due to Illness
If you miss deadlines or presentations due to illness, please bring the instructor a doctor's note that indicates not only that you had a medical consultation but also reports the actual illness that was diagnosed. If such note is provided, the instructor will be happy to make accomodations and extend deadlines. Otherwise, all other policies apply (e.g., late deliverables).
Exams
There will not be a midterm or final exam for this course.
Sharing of Course Materials is Prohibited
No materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (quizzes, exams, papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may be shared online or with anyone outside of the class unless you have my explicit, written permission. Unauthorized sharing of materials promotes cheating. It is a violation of the University’s Student Honor Code and an act of academic dishonesty. I am well aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.
FERPA and Class Recordings
Class recordings, if available, are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.
Standard UT Austin Course Information and Policies
Academic Honor Code: You are encouraged to discuss assignments with classmates, but anything submitted must reflect your own, original work. If in doubt, ask the instructor. Plagiarism and similar conduct represents a serious violation of UT's Honor Code and standards of conduct.
Students who violate University rules on academic dishonesty are subject to severe disciplinary penalties, such as automatically failing the course and potentially being dismissed from the University. Please do not take the risk. We are required to automatically report any suspected case to central administration for investigation and disciplinary hearings. Honor code violations ultimately harm yourself as well as other students, and the integrity of the University.
Academic honesty is strictly enforced.
Notice about students with disabilities: The University of Texas at Austin provides appropriate accommodations for qualified students with disabilities. Please check UT Disability and Access. If they certify your needs, we will work with you to make appropriate arrangements. Any students requiring assistance in evacuation must inform the instructor in writing of their needs during the first week of classes.
Coping with stress and personal hardships: The Counseling and Mental Health Center offers a variety of services for students, including both individual counselling and groups and classes, to provide support and assistance for anyone coping with difficult issues in their personal lives. As mentioned above, life brings unexpected surprises to all of us. If you are facing any personal difficulties in coping with challenges facing you, definitely consider the various services offered and do not be shy to take advantage of them if they might help. These services exist to be used.
Notice about missed work due to religious holy days: A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
Electronic Mail Notification Policy: In this course e-mail, Canvas will be used as a means of communication with students. You will be responsible for checking your e-mail regularly for class work and announcements. If you are an employee of the University, your e-mail address in Canvas is your employee address.
The University has an official e-mail student notification policy. It is the student's responsibility to keep the University informed as to changes in his or her e-mail address. Students are expected to check e-mail on a frequent and regular basis in order to stay current with University-related communications, recognizing that certain communications may be time-critical.